If you have ever deployed a Large Language Model (LLM) or a complex computer vision model to an Android device, you have likely encountered the "Performance Paradox." On your high-end development workstation, the model runs with lightning speed. But on a user's mid-range device, the frame rate stutters, the device becomes uncomfortably warm, and the "intelligent" features suddenly feel sluggish.
The reason is simple: Benchmarking on the edge is fundamentally different from benchmarking in a controlled cloud environment.
In the cloud, you operate in a world of predictable, homogeneous hardware where performance is a linear function of compute availability. On Android, you are operating in a "fragmented ecosystem of constraints." You aren't just measuring how fast a model runs; you are measuring how a model survives the volatile intersection of thermal throttling, aggressive kernel-level memory management, and heterogeneous hardware scheduling.
To build production-ready AI, you must move beyond simple "execution time" metrics. This guide will walk you through the paradigm shift required to master Edge AI benchmarking.
The Hardware Landscape: NPU, GPU, and DSP
